Research And Realization Of Personalized Product Recommendation System Based On Deep Learning

With the rapid development of Internet and e-commerce,online shopping has gradually become one of the important roles in our daily life,but in the face of the increasing amount of data,users have to pay more time to screen the content they need.It leades to the phenomenon of information overload.In the field of e-commerce,how to help consumers find satisfactory products quickly is a problem that must be considered by major online shopping websites.In recent years,deep learning has achieved ideal results in natural language processing,image processing and other fields.The application of deep learning method in the field of recommendation brings a new opportunity for the development of recommendation technology.Therefore,this paper proposes a hybrid recommendation model based on deep learning method,and uses this model to build a personalized commodity recommendation system.First of all,in order to solve the problem of information overload,combined with the existing research,we use the deep learning method to analyze the user preferences,make the score prediction of the goods and then recommend the goods with higher scores to the users.A hybrid recommendation model of SRNN and DAE is proposed,which improves the operation speed of the model.Then,in order to improve the accuracy of recommendation,aiming at the problem that SRNN only pays attention to the timing sequence of input items,the SRNN is optimized and improved by scoring interest factor,the user preference for goods in the sequence is extracted and the user similarity is embedded into the model as auxiliary information.By comparing with the user-based collaborative filtering algorithm and the recommendation model based on convolution neural network,the results show that the running efficiency and effect of the recommendation model proposed in this paper are higher than that of the comparison model.Finally,with the hybrid recommendation model proposed in this paper as the core,a personalized commodity recommendation system based on deep learning is designed and implemented,the system is displayed and the function is tested at the same time.It is verified that the system can achieve accurate commodity recommendation to users.